Chlorophyll-a (Chl-a) is a key indicator of eutrophication and algal blooms in freshwater ecosystems. This study aimed to improve Chl-a prediction through a novel integrative approach that combines the optical and structural characteristics of dissolved organic matter (DOM) with machine learning and explainable artificial intelligence techniques. Weekly data were collected over one year (Apr 2023–Apr 2024) from four sites in the Yanghwa Stream, South Korea. Machine learning models (Random Forest, Gradient Boosting Machine, and Extreme Gradient Boosting) were developed using 34 input variables encompassing 15 traditional water quality parameters, 8 ultraviolet–visible (UV–Vis) optical indices, and 11 liquid chromatography–organic carbon detection (LC–OCD) components. Extreme Gradient Boosting showed the best performance (R 2 = 0.7318, RMSE = 4.3827). SHapley Additive exPlanations analysis identified total nitrogen, total organic carbon, spectral slope ratio (S R ; ratio of S 275–295 to S 350–400 ), and Molecularity (nominal average molecular weight of the humic substances fraction derived from LC-OCD) as major contributors, highlighting the importance of DOM composition in Chl-a variability. Counterfactual analysis further revealed model-guided adjustment scenarios for reducing high Chl-a concentrations to below the threshold (12.2 mg/m 3 ; 75th percentile), emphasizing the importance of variables such as total nitrogen, pHosphate, Molecularity, and S R as key indicators within the learned model. In conclusion, this integrated approach combining DOM characteristics with explainable artificial intelligence techniques enhances prediction accuracy and interpretability and provides specific and practical strategies for the effective control of Chl-a in freshwater systems.